Zhao Chengyang
Zhao Chengyang (also known online as Chayenne Zhao) is a member of technical staff at RadixArk, a San Francisco-based AI infrastructure company launched in May 2026 that develops and commercializes SGLang. He is one of the principal maintainers of SGLang, an open-source, high-performance serving framework for large language models, and focuses on optimizing reinforcement learning training pipelines and large-scale inference systems. He studied computer science at Tsinghua University before pursuing a Ph.D. at UCLA and is recognized for his technical contributions to end-to-end RL infrastructure and multi-turn agent inference at frontier scale.
“SGLang is deployed on over 400,000 GPUs globally for production-level inference — one of the largest open-source engines of this generation.”
Source→“TileLang is now being used by frontier labs as one of the default choices for implementing algorithms — this has happened in roughly the last year and a half.”
Source→“Claude's generation of models, from 4.5 onwards, in multi-step agentic code performance compared to before, has truly improved enormously.”
Source→“In inference, the gap between proprietary and open-source is not that large. But in training, proprietary training still leads open-source by quite a bit. A model might launch in February, but it might take until May or June before an open-source RL framework can actually run its RL pipeline.”
Source→“Our team did substantial engineering optimization and successfully ran both the inference and RL pipelines on the day DeepSeek V4 was released.”
Source→“TileLang has now been adopted by frontier labs as one of the default choices for algorithm implementation.”
Source→“If 3.0 gets into WeChat, the competitive landscape could get very interesting.”
Source→“TileLang is now being used by cutting-edge labs as one of their primary choices for algorithm implementation.”
Source→“Our team made substantial engineering optimizations and successfully ran both the inference and RL pipelines on the day V4 was released.”
Source→“For example, MTP is quite critical for voice — like when you open Doubao and talk to it, the speed at which it produces the first piece of audio is very fast. Very unfortunately, on the open-source side we haven't done this nearly as well.”
Source→AI-extracted from podcast / newsletter / paper summaries. May contain errors.